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The Genotype-Tissue Expression project

We meet a new frontier in biomedical research with publications from the Genotype-Tissue Expression (GTEx) Consortium, that of cataloguing genetic variation and its influence on gene expression within and between all major tissues in the human body. The GTEx project was proposed in 2008 with the lofty goals of establishing a resource database and associated tissue biobank to study the relationship between genetic variation and gene expression in all major human tissues across 1000 individuals. This nearly decade long effort now brings the largest multi-tissue research study using postmortem donors, which entailed overcoming many challenges including developing protocols to obtain high quality biospecimens as well as establishing a framework for the ethical, legal and social issues surrounding postmortem donation.

In this second phase of the project, GTEx profiles genetic variation, gene expression, histological and clinical data for 449 human donors across 44 tissues. The authors use the GTEx data to analyze the influence of genetic variation on gene expression within and between tissues and individuals. These studies have helped to crack the regulatory code of our genome, demonstrating that the expression of nearly all genes are regulated by genetic variation, most of which is located close to the affected gene.

We are pleased to present this
Nature collection of news, commentary and research publications across Nature journals and
Genome Research for the second phase of the GTEx project.

- Orli Bahcall, Senior Editor,
Nature

Listen to GTEx researchers discuss the challenges in establishing the GTEx project, including a framework for research on tissues from postmortem donors. Hear from grieving family members about their experience in contributing to this this genetics project. This and more on our Nature
Podcast.

A collection of papers catalogues the associations between genetic variation and gene expression in healthy tissues – the largest analysis of this kind so far. See Article p.204 & Letters p.239, p.244 & p.249

Genetic variants have been associated with myriad molecular phenotypes that provide new insight into the range of mechanisms underlying genetic traits and diseases. Identifying any particular genetic variant's cascade of effects, from molecule to individual, requires assaying multiple layers of molecular complexity. We introduce the Enhancing GTEx (eGTEx) project that extends the GTEx project to combine gene expression with additional intermediate molecular measurements on the same tissues to provide a resource for studying how genetic differences cascade through molecular phenotypes to impact human health.

The authors show that rare genetic variants contribute to large gene expression changes across diverse human tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Multiple transcriptome approaches, including single-cell sequencing, demonstrate that escape from X chromosome inactivation is widespread and occasionally variable between cells, chromosomes, and tissues, resulting in sex-biased expression of at least 60 genes and potentially contributing to sex-specific differences in health and disease.

Using the GTEx data and others, a comprehensive analysis of adenosine-to-inosine RNA editing in mammals is presented; targets of the various ADAR enzymes are identified, as are several potential regulators of editing, such as AIMP2.

This study presents a new approach to estimate the tissues contributing to the genetic causality for complex traits and diseases. The method assesses tissue sharing of eQTLs among 44 tissues and then uses these tissue-sharing estimates to infer the tissues where trait-associated variants likely exert their function.

Methods publications

RNA levels in post-mortem tissue can differ greatly from those before death. Studying the effect of post-mortem interval on the transcriptome in 36 human tissues, Ferreira et al. find that the response to death is largely tissue-specific and develop a model to predict time since death based on RNA data.

Multivariate adaptive shrinkage (mash) is a method for estimating and testing multiple effects in multiple conditions. When applied to GTEx data, mash can be used to analyze sharing of eQTL effects by examining variation in effect sizes.